Search Results for author:
Found 7 papers, 4 papers with code
Screentone-Aware Manga Super-Resolution Using DeepLearning
In this paper, we aims to address this issue by first classifying the regions and lines of different screentone in the manga using deep learning algorithm, then using corresponding super-resolution models for quality enhancement based on the different classifications of each block, and finally combining them to obtain images that maintain the meaning of screentone and lines in the manga while improving image resolution.
GenISP: Neural ISP for Low-Light Machine Cognition
We propose to improve generalization to unseen camera sensors by implementing a minimal neural ISP pipeline for machine cognition, named GenISP, that explicitly incorporates Color Space Transformation to a device-independent color space.
NOD: Taking a Closer Look at Detection under Extreme Low-Light Conditions with Night Object Detection Dataset
In our work, we take a closer look at object detection in low light.
GrateTile: Efficient Sparse Tensor Tiling for CNN Processing
We propose GrateTile, an efficient, hardwarefriendly data storage scheme for sparse CNN feature maps (activations).
xCos: An Explainable Cosine Metric for Face Verification Task
We study the XAI (explainable AI) on the face recognition task, particularly the face verification here.
Explainable Artificial Intelligence (XAI)
Face Recognition
+1
Unrolled Memory Inner-Products: An Abstract GPU Operator for Efficient Vision-Related Computations
Recently, convolutional neural networks (CNNs) have achieved great success in fields such as computer vision, natural language processing, and artificial intelligence.
Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel Removal
Deep Convolutional Neural Networks (CNNs) are widely employed in modern computer vision algorithms, where the input image is convolved iteratively by many kernels to extract the knowledge behind it.
